Front-end developer, designer, dabbler, and avid user of the superpowered information superhighway.
I don't necessarily believe or disbelieve in the final 1% taking the longest in this case – there are too many variables to make a confident prediction. However, it does tend to be a common occurrence.
It could very well be that the 1% before the final 1% takes the longest. Based on the past few years, progress in the AI space has been made fairly steadily, so it could also be that it continues at just this pace until that last 1% is hit, and then exponential takeoff occurs.
You could also have a takeoff event that carries from now till 99%, which is then followed by the final 1% taking a long period.
A typical exponential takeoff is, of course, very possible as well.
A great collection of posts there. Plenty of useful stuff.
This prompted me to write down and keep track of my own usage:
https://vale.rocks/posts/ai-usage
Predicting AGI/ASI timelines is highly speculative and unviable. Ultimately, there are too many unknowns and complex variables at play. Any timeline must deal with systems and consequences multiple steps out, where tiny initial errors compound dramatically. A range can be somewhat reasonable, a more specific figure less so, and accurately predicting the consequences of the final event when it comes to pass even further improbable. It is simply impractical to come up with an accurate timeline with the knowledge we currently have.
Despite this, timelines are popular – both with the general AI hype crowd and those more informed. People don't seem to penalise incorrect timelines – as evidenced by the many predicted dates we've seen pass without event. Thus, there's little downside to proposing a timeline, even an outrageous one. If it's wrong, it's largely forgotten. If it's right, you're lauded a prophet. The nebulous definitions of "AGI" and "ASI" also offer an out. One can always argue the achieved system doesn't meet their specific definition or point to the AI Effect.
I suppose @gwern's fantastic work on The Scaling Hypothesis is evidence of how an accurate prediction can significantly boost credibility and personal notoriety. Proposing timelines gets attention. Anyone noteworthy with a timeline becomes the centre of discussion, especially if their proposal is on the extremes of the spectrum.
The incentives for making timeline predictions seem heavily weighted towards upside, regardless of the actual predictive power or accuracy. Plenty to gain; not much to lose.
If many independent actors are working on AI capabilities, even if each team has decent safety intentions within their own project, is there a fundamental coordination problem that makes the overall landscape unsafe? A case where the sum of the whole is flawed, unsafe, and/or dangerous and thus doesn't equal collective safety?
I think people seem to downplay that when artificial intelligence companies release new models/features, they tend to do so with minimal guardrails.
I don't think it is hyperbole to suggest this is done for the PR boost gained by spurring online discussion, though it could also just be part of the churn and rush to appear on top where sound guardrails are not considered a necessity. Either way, models tend to become less controversial and more presentable over time.
Recently OpenAI released their GPT-4o image generation with rather relaxed guardrails (it being able to generate political content and images of celebrities without consent). This came hot off the heels of Google's latest Imagen model, so there was reason to rush to market and 'one-up' Google.
Obviously much of AI risk is centred around swift progress and companies prioritising that progress over safety, but minimising safety specifically for the sake of public perception and marketing strikes me as something we are moving closer towards.
This triggers two main thoughts for me:
Taking time away from something and then returning to it later often reveals flaws otherwise unseen. I've been thinking about how to gain the same benefit without needing to take time away.
Changing perspective is the obvious approach.
In art and design, flipping a canvas often forces a reevaluation and reveals much that the eye has grown blind to. Inverting colours, switching to greyscale, obscuring, etc, can have a similar effect.
When writing, speaking written words aloud often helps in identifying flaws.
Similarly, explaining why you've done something – à la rubber duck debugging – can weed out things that don't make sense.